alps

说明：  压缩包内含有《MATLAB 6.0时尚创作百例》的源程序，很实用的。(Compressed packets contain MATLAB 6.0 fashion 100 cases of creation, the source, it is practical.)

本站以C语言和Java语言、嵌入式开发、算法实现为主，源码数量达几十万个，有200多个分类，很多都是本站会员的私家珍藏，互联网上其他地方是找不到的(The Gabor filter is basically a Gaussian (with variances sx and sy along x and y-axes respectively) modulated by a complex sinusoid (with centre frequencies U and V along x and y-axes respectively) described by the following equation)

说明：  该程序用于生成zc序列(在LTE中的RACH过程和下行同步过程中使用的序列)。(this code is used to generate ZC sequence，whic is commonly used in the RACH procedure and the down link synchronization.)

混沌动力学初步对于刚开始接触混沌的人来说是个很好的书，希望对大家有用(Chaotic dynamics chaotic initial contact with the person who started it is a very good book, we want to be useful)

三自由度自行车模型，非线性轮胎模型，考虑纵向加速度(Three DOF bicycle model, nonlinear tire model, considering the longitudinal acceleration)

这是matlab编写的3个常用机器学习分类器代码。其中包括了： 1）PCA 分类其；2）LDA分类器：3）naive贝叶斯分类器。 3个算法的实现参考了《Introduction to Machine Learning》。 除了这3个分类算法的实现外，代码里面还包含了用于测试的main.m 主程序和一个实验的简要报告。实验在著名数据集acoustic_train_data 上进行。(This source code includes the implementation of three famous classifiers in machine learning. They are 1) PCA, 2) LDA and 3) Naive Bayesian. The detail theory behind these classifier can be found text book <<Introduction to Machine Learning>>. Besides the implementation of these 3 algorithms, the main.m for testing and a brief testing report is also included. (The testing is on data set acoustic_train_data) Enjoy:))

matlab图像集合变化与图像增强操作以及报告 (MATLAB image collections and image enhancement operations and reporting)

说明：  总体最小二乘法的ESPRIT方法，这种算法的估计精度明显好于原来的ESPRIT算法。可以很好的估计出信号的角度。仿真中用的是均匀阵列。(Total least squares method of the ESPRIT method, the algorithm of the estimation accuracy significantly better than the original ESPRIT algorithm. Can be a very good signal to estimate the angle. Simulation using the uniform array.)

免疫算法的源程序，对学习免疫算法很有用！！！(Immune source, the immune algorithm for learning useful! ! !)

Matlab tool for segmentation and re-creation of H-MRS volumes of interest in MRI image stack